• Mir Henglin, Gillian Stein, Pavel V Hushcha, Jasper Snoek, Alexander B Wiltschko, and Susan Cheng.
• From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.). scheng@rics.bwh.harvard.edu.
• Circ Cardiovasc Imaging. 2017 Oct 1; 10 (10).

AbstractCardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.© 2017 American Heart Association, Inc.

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